Fault Detection for Multi-Rate Sampling Systems Based on Dynamic Principal Component Analysis

被引：0

作者：

Li, Zhijun ^{[1
]}

Liang, Lele ^{[1
]}

Han, Cunwu ^{[1
]}

Guo, Fumin ^{[2
]}

Sun, Dehui ^{[1
]}

机构：

[1] North China Univ Technol, Key Lab Fieldbus & Automat Technol Beijing, Beijing 100144, Peoples R China

[2] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China

来源：

PROCEEDINGS OF THE 2016 12TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2016年

关键词：

MISSING DATA;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For the multi-rate sampling systems with time series correlation data, a multi-rate fault detection algorithm based dynamic principal component analysis is proposed. The same sampling rate can be achieved in the algorithm by interpolation-filter-decimation, and then dynamic principal component analysis is implemented. The proposed method not only makes full use of the samples in a large number of incomplete data but also reduces the multi-rate sampling bias which caused by the data, the offline modeling and online monitoring strategies are also proposed. Finally, the Tennessee Eastman process is used to test the effectiveness of the proposed algorithm, and the simulation results show that the proposed method has a better performance in multi-rate sampling systems with serial correlation data than other fault detection methods based on principal component analysis.

引用

页码：1307 / 1311

页数：5

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